UID:
almafu_9960819776302883
Format:
1 online resource (42 pages) :
,
digital, PDF file(s).
Edition:
1st ed.
ISBN:
1-009-29639-6
,
1-009-29638-8
,
1-009-29640-X
Series Statement:
Cambridge elements. Elements in law, economics and politics
Content:
Do US Circuit Courts' decisions on criminal appeals influence sentence lengths imposed by US District Courts? This Element explores the use of high-dimensional instrumental variables to estimate this causal relationship. Using judge characteristics as instruments, this Element implements two-stage models on court sentencing data for the years 1991 through 2013. This Element finds that Democratic, Jewish judges tend to favor criminal defendants, while Catholic judges tend to rule against them. This Element also finds from experiments that prosecutors backlash to Circuit Court rulings while District Court judges comply. Methodologically, this Element demonstrates the applicability of deep instrumental variables to legal data.
Note:
Title from publisher's bibliographic system (viewed on 02 Sep 2022).
,
Cover -- Title Page -- Copyright Page -- Deep IV in Law -- Contents -- 1 Introduction -- 2 Theoretical Framework -- 2.1 Related Works on Law -- 2.2 Related Work on Machine Learning -- 3 Data Set -- 3.1 Data Set Description -- 3.1.1 Cleaned Circuit Court Case Data -- 3.1.2 Judge Biographical Characteristics -- 3.1.3 District Courts Sentencing Data -- 3.1.4 Circuit Cases Metadata -- 3.2 Data Preprocessing -- 3.2.1 Feature Engineering -- 3.2.2 Representing Case Text as Data -- 3.2.3 Normalization and Splitting Data -- 4 Empirical Model -- 5 Results -- 5.1 Deep OLS -- 5.2 Deep Reduced Form -- 5.3 Deep2SLS -- 5.3.1 First Stage -- 5.3.2 Second Stage -- 5.3.3 Correspondence to Theoretical Models -- 5.4 Discussion -- 6 On the Practical Use of Deep IV for Law and Economics -- 7 Limitations from a Computer Science Perspective -- 8 Limitations from an Economics Perspective -- 9 Potential Future Work -- Appendix -- A.1 Distribution of Predicted Sentence Length Change -- A.2 Detailed Description of the Data Set and Features -- References -- Acknowledgments.
Additional Edition:
ISBN 9781009296373
Language:
English
URL:
https://doi.org/10.1017/9781009296403
Bookmarklink
